A Comparison of Stochastic Processes and Artificial Neural Networks for Canonical Correlation Analysis

2006 ◽  
Author(s):  
Pei Ling Lai ◽  
G. Leen ◽  
C. Fyfe
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Correlation Analysis ◽  
Stochastic Processes ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Artificial Neural

scholarly journals Integrated approach to the planning of energy consumption by non-traction railway consumers

2018 ◽  
Vol 239 ◽  
pp. 01056 ◽  
Author(s):  
Alexander Galkin ◽  
Alexey Kovalev ◽  
Timur Shayuhov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Correlation Analysis ◽  
Power Consumption ◽  
Systematic Approach ◽  
Integrated Approach ◽  
Expert Assessments ◽  
The Subject ◽  
Artificial Neural ◽  
High Degree

Non-traction railway load consumes a significant amount of electricity. Russian Railways supplies electricity not only to its structural units but also to other consumers. Private houses, individual entrepreneurs and small production facilities located near the railway are powered by the RZD. It is very important for the company to plan consumption for non- traction needs. The subject of the study in the paper is a systematic approach to the planning of power consumption using the mathematical apparatus of artificial neural networks, correlation analysis and the method of expert assessments. The method of expert assessments allows identifying the most significant factors that affect the consumption of electricity. It is necessary to attract experienced professionals in the field of electricity, working at a particular enterprise. They are able to determine with a high degree of accuracy those factors that have a significant impact on the consumption of the organization. Correlation analysis allows you to mathematically check the degree of influence of a single factor on the resulting value. The apparatus of artificial neural networks allows building a forecast of power consumption, taking into account the influence of external factors. The authors propose to use a systematic approach to the planning of power consumption. It is necessary to combine three tools: the method of expert assessments, correlation analysis and artificial neural networks. The combination of these tools will improve the accuracy of power consumption planning and, as a result, will lead to increased economic efficiency due to the rational consumption of electricity.

Artificial neural networks as approximators of stochastic processes

1999 ◽  
Vol 12 (4-5) ◽  
pp. 647-658 ◽  
Author(s):  
M.R Belli ◽  
M Conti ◽  
P Crippa ◽  
C Turchetti
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stochastic Processes ◽  
Artificial Neural

Artificial Neural Networks as Approximators of Stochastic Processes

1998 ◽  
pp. 627-632
Author(s):  
M. R. Belli ◽  
M. Conti ◽  
P. Crippa ◽  
S. Orcioni ◽  
C. Turchetti
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stochastic Processes ◽  
Artificial Neural
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